Rejoinder on “Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks”

In this paper we answer to the comments provided by Fabio Cozman, Marco Zaffalon, Giorgio Corani, and Didier Dubois on our paper ‘Imprecise Probability Models for Learning Multinomial Distributions from Data. Applications to Learning Credal Networks’. The main topics we have considered are: regularity, the learning principle, the trade-off between prior imprecision and learning, strong symmetry, and the properties of ISSDM for learning graphical conditional independence models.